Solving the Master Data Management challenge

Master Data Management, or MDM, is a common issue for customers across all industries — having multiple data sources makes it difficult to identify one source of truth for the data. A typical business might encounter these types of data issues:

Data Management challenge

MDM Technology assessment

When deciding whether to build vs buy a solution, the following are key factors to keep in mind when completing a technical assessment:

  • Data Modeling: Managing any complex relationships and map to the master customer information requirements of the whole organization, not just selected areas
  • Information Quality Management: Well-integrated facilities for cleansing, matching, linking and identifying data from different sources
  • Loading, Integration & Synchronization: The solution should support the bidirectional transfer of data between the central system and peripheral systems in batch and real-time modes
  • Business Services & Workflow: Data encapsulation and base for SOA applications
  • Leveraging Current Technical Landscape: Evaluation of existing master data hub
  • Performance, Scalability & Availability: Benchmarks available for batch and real-time integration. Horizontal/Vertical scaling available on physical and virtual environments
  • Manageability & Security: Reporting on activity & data quality, integration with systems management, manage access rights and privacy
  • Flexible architecture: MDM Database – CRM Vendor specific, Industry Vendor Specific or a New data hub ,Single or Multi-domain MDM solution

Additional challenges to consider as you try to make sense of all your data:


  • Multiple primary sources of data used by the team (Reporting Server, Marketing Data Warehouse, Marketing Analytics)
  • Data redundancy, data quality, contact, account mismatch, fuzzy match issues recorded as part of the conversations
  • Team generates different datasets to support daily operations (Leads, Accounts, Sales, Contracts, Customers, Owners, …)
  • Team spends a lot of time debugging the datasets, because most of the business logic is embedded within the reporting layer or within the feed generation scripts (this should be part of ETL)
  • Lead data consolidation issues
    • Data coming from multiple vendors
    • Can’t match the information to the current contracts, accounts, address etc.
    • Salesforce lead suppression


  • Team spends a lot of time on data consolidation and validations (BI Reports)
  • Datasets take a lot of time to generate
  • Lack of automation around process of feed generation and notifications
  • Can’t trust the data

Platform & Infrastructure

  • Customer may have best of the tools for analytics, but are not using to their full potential
  • Data consolidation is required, and that can be leveraged more for BI and Analytics
  • Lack of interactive reporting

Implementation models

There are a number of models for implementing a technology solution for master data management. These depend on an organization’s core business, its corporate structure and its goals. These include:

  • Source of record
  • Registry
  • Consolidation
  • Coexistence/ create golden record
  • Transaction/centralized

Master Data Management (MDM) can help build a 360-degree view of key business information, bringing together data from Sales & Marketing, Regulatory Affairs, Finance, IT, Operations and Manufacturing. This allows you to take full advantage of your organization’s data for better business outcomes.

Data Management challenge 1

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